Atrial fibrillation detection using support vector machine

Nuryani, Nuryani; Harjito, Bambang; Yahya, Iwan; Lestari, Anik

doi:10.1109/icevtimece.2015.7496672

Cited by 11 publications

(9 citation statements)

References 25 publications

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“…Two features: the average of RR differences in a defined duration, and the standard deviation of differences in a defined duration, were examined as the inputs of the classifier based on SVM with radial basis function in [40]. The proposed method showed following performance on the MIT-BIH AF database: Se = 95.81%, Sp = 98.44% and CA = 97.50%.…”

Section: Discussionmentioning

confidence: 99%

“…The statistical test (Kolmogorov-Smirnov) was used in [33] to check if the density histograms of the test data differ from the standard density ones prepared as a template of AF episodes. In order to differentiate between AF and non-AF patterns the various classification methods have been applied: Neyman-Pearson detector [31], Random Forest (RF) model and k-nearest neighbors classifier [32], Support Vector Machine (SVM) with promising results reported in [39][40][41], as well as artificial neural network [42], also with interval transition matrices as an input [43].…”

Section: Introductionmentioning

confidence: 99%

“…Higher sensitivity was ensured by the HR-based approach, while ECG-based algorithm provided improved specificity and classification accuracy. The classifier based on SVM with radial basis function was proposed in [40], with two features as the inputs: the average of RR differences and the standard deviation of differences in a defined duration. The same SVM classifier was employed in [41].…”

Section: Introductionmentioning

confidence: 99%

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Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Czabański

Horoba

Wróbel

et al. 2020

Sensors

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Atrial fibrillation (AF) is a serious heart arrhythmia leading to a significant increase of the risk for occurrence of ischemic stroke. Clinically, the AF episode is recognized in an electrocardiogram. However, detection of asymptomatic AF, which requires a long-term monitoring, is more efficient when based on irregularity of beat-to-beat intervals estimated by the heart rate (HR) features. Automated classification of heartbeats into AF and non-AF by means of the Lagrangian Support Vector Machine has been proposed. The classifier input vector consisted of sixteen features, including four coefficients very sensitive to beat-to-beat heart changes, taken from the fetal heart rate analysis in perinatal medicine. Effectiveness of the proposed classifier has been verified on the MIT-BIH Atrial Fibrillation Database. Designing of the LSVM classifier using very large number of feature vectors requires extreme computational efforts. Therefore, an original approach has been proposed to determine a training set of the smallest possible size that still would guarantee a high quality of AF detection. It enables to obtain satisfactory results using only 1.39% of all heartbeats as the training data. Post-processing stage based on aggregation of classified heartbeats into AF episodes has been applied to provide more reliable information on patient risk. Results obtained during the testing phase showed the sensitivity of 98.94%, positive predictive value of 98.39%, and classification accuracy of 98.86%.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Czabański

Horoba

Wróbel

et al. 2020

Sensors

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“…Czabanski et al [ 26 ] designed an LSVM classifier with sixteen input features extracted from HRV signals, which achieved the training of a few samples with an excellent classification performance; the classifier had an accuracy rate of 98.86%. Nuryani et al [ 27 ] tested different SVM structures, used radial basis function (RBF), and examined two ECG features as SVM inputs (the average of RR intervals and standard deviation of the RR intervals). The final sensitivity, specificity, and accuracy were 95.81%, 98.44%, and 97.50%, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Wang

Yan

Yang

et al. 2021

Sensors

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Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.

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“…• desvio padrão (Maji;Mitra;Pal, 2014;Meža, 2015;Nuryani et al, 2015;Kalsi;Prakash, 2016;Shan et al, 2016);…”

Section: Parâmetros Para a Detecção De Fibrilação Atrialmentioning

confidence: 99%

Classificação de segmentos de eletrocardiograma normais e com fibrilação atrial: estudo metodológico.

Jeong¹

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Primeiramente, gostaria de agradecer aos meus pais, que me incentivaram e apoiaram durante a minha formação. Principalmente a minha mãe que infelizmente acabou partindo, durante o decorrer do mestrado.Aos meus irmãos que tem me dado suporte. Seus incentivos foram fundamentais para que eu não perdesse os prazos de submissão e de apresentação do artigo no congresso.À Profa. Dra. Cinthia Itiki, por toda a sua paciência e excelente orientação. Apesar de todas as dificuldades e situações durante o período do mestrado, esteve constantemente me orientando e ajudando.

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Atrial fibrillation detection using support vector machine

Cited by 11 publications

References 25 publications

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

Detection of Atrial Fibrillation Episodes in Long-Term Heart Rhythm Signals Using a Support Vector Machine

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Classificação de segmentos de eletrocardiograma normais e com fibrilação atrial: estudo metodológico.

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